Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this
method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore,
the purpose of this tutorial was to show how a three-level random effects model can be applied to meta-analytic models in
R using the rma.mv function of the metafor package. This application is illustrated by taking the reader through a step-by-step
guide to the multilevel analyses comprising the steps of (1) organizing a data file; (2) setting up the R environment; (3)
calculating an overall effect; (4) examining heterogeneity of within-study variance and between-study variance; (5) performing
categorical and continuous moderator analyses; and (6) examining a multiple moderator model. By example, the authors demonstrate
how the multilevel approach can be applied to meta-analytically examining the association between mental health disorders
of juveniles and juvenile offender recidivism. In our opinion, the rma.mv function of the metafor package provides an easy
and flexible way of applying a multi-level structure to meta-analytic models in R. Further, the multilevel meta-analytic models
can be easily extended so that the potential moderating influence of variables can be examined.

Fitting Three-Level Meta-Analytic Models in R: A Step-by-Step Tutorial: 176301_Assink & Wibbelink (in press) - Fitting Three-Level
Meta-Analytic Models in R.pdf: This article has been peer reviewed and accepted for publication. It is in production and has
not been edited, so may differ from the final published form.With appendix online

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